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About the client
A fast-growing AI cloud company with strong roots in open-source AI research. Its platform supports some of the most widely used AI products on the market and runs on one of the world's largest GPU fleets. At this scale, infrastructure operations are increasingly handled by software and autonomous AI agents, and the company is investing heavily in building them.
The role
You'll join the Infra Agent Systems team, which builds production AI agents that diagnose hardware failures, investigate incidents, and automate operations across the GPU fleet, along with the platform they run on. The core challenge is making AI agents trustworthy enough to operate production infrastructure at scale. It's foundational work that hasn't really been done before.
What you'll do
- Design and build production AI agents that diagnose, investigate, and help remediate infrastructure issues across a massive GPU fleet
- Build the distributed services, orchestration framework, knowledge graph, and retrieval systems that power these agents
- Keep agent knowledge accurate and up to date, and combine telemetry, infrastructure state, and incident history to build fleet intelligence
- Improve agent performance through evaluations, improved tools, improved retrieval, and production feedback loops
- Own services end-to-end, from architecture and implementation to deployment, observability, and production operations
Must-have requirements
- 5+ years building production backend systems, distributed systems, or infrastructure platforms
- 5+ years of production experience with Python, Go, or TypeScript
- Hands-on experience building AI agents (harness, tools, memory, evaluation), not only RAG pipelines or LLM-powered product features
- Proven results in significantly improving agent accuracy and effectiveness
- Experience designing human-in-the-loop controls and access boundaries for agents acting on live systems
- Experience with Kubernetes, GitOps (e.g., ArgoCD), infrastructure-as-code, and cloud platforms
- Daily use of AI coding tools in your workflow
- Builder mindset and high ownership: you've played a major role in designing and shipping complex systems
- Strong communication skills and full professional English proficiency
- GPU infrastructure, data centers, bare-metal systems, hardware failure modes, BMC/IPMI or cluster schedulers
- Building knowledge graphs; experience with graph databases
- Event-driven systems and messaging (NATS, Kafka)
- Observability platforms such as Prometheus and Grafana
- Building evaluation frameworks for LLM-powered systems
- Training or fine-tuning small specialized models, including classical ML such as anomaly detection
Team and setup
- Team of 10+ engineers, working directly with principal engineers and team leads.
- Agents are used daily by infrastructure and data center teams through APIs, CLI, dashboards, and Slack
- Near-term focus: improving retrieval, the memory backend and the agent harness, and training small specialized models on the company's own infrastructure
